Title: Assignment one for Reproducible Research
NOTE:Before executing the code, please download the file from Coursera website: Activity monitoring data
Step 1: Loading and preprocessing the data
unziprepdata<-unzip("repdata-data-activity.zip")
repdata<-read.csv(unziprepdata, head=T, na.strings="NA")
head(repdata)## steps date interval
## 1 NA 2012-10-01 0
## 2 NA 2012-10-01 5
## 3 NA 2012-10-01 10
## 4 NA 2012-10-01 15
## 5 NA 2012-10-01 20
## 6 NA 2012-10-01 25str(repdata)## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Factor w/ 61 levels "2012-10-01","2012-10-02",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...Step 2: What is the mean total number of steps taken per day?
2.1: Calculate the total number of steps taken per day
totalStepPerDay<-tapply(repdata$steps, repdata$date, sum)
totalStepPerDay## 2012-10-01 2012-10-02 2012-10-03 2012-10-04 2012-10-05 2012-10-06
## NA 126 11352 12116 13294 15420
## 2012-10-07 2012-10-08 2012-10-09 2012-10-10 2012-10-11 2012-10-12
## 11015 NA 12811 9900 10304 17382
## 2012-10-13 2012-10-14 2012-10-15 2012-10-16 2012-10-17 2012-10-18
## 12426 15098 10139 15084 13452 10056
## 2012-10-19 2012-10-20 2012-10-21 2012-10-22 2012-10-23 2012-10-24
## 11829 10395 8821 13460 8918 8355
## 2012-10-25 2012-10-26 2012-10-27 2012-10-28 2012-10-29 2012-10-30
## 2492 6778 10119 11458 5018 9819
## 2012-10-31 2012-11-01 2012-11-02 2012-11-03 2012-11-04 2012-11-05
## 15414 NA 10600 10571 NA 10439
## 2012-11-06 2012-11-07 2012-11-08 2012-11-09 2012-11-10 2012-11-11
## 8334 12883 3219 NA NA 12608
## 2012-11-12 2012-11-13 2012-11-14 2012-11-15 2012-11-16 2012-11-17
## 10765 7336 NA 41 5441 14339
## 2012-11-18 2012-11-19 2012-11-20 2012-11-21 2012-11-22 2012-11-23
## 15110 8841 4472 12787 20427 21194
## 2012-11-24 2012-11-25 2012-11-26 2012-11-27 2012-11-28 2012-11-29
## 14478 11834 11162 13646 10183 7047
## 2012-11-30
## NA2.2: Make a histogram of the total number of steps taken each day
hist(totalStepPerDay, col="blue",main="Total number of steps")
2.3: Calculate the mean and median of the total number of steps taken per day
## Mean of the total number of steps taken per day
mean(totalStepPerDay, na.rm=TRUE)## [1] 10766.19## Median of the total number of steps taken per day
median(totalStepPerDay, na.rm=TRUE)## [1] 10765step 3: What is the average daily activity pattern?
3.1: A time series plot
interval<-unique(repdata$interval)
avgstepsbyinterval<-sapply(split(repdata$steps, repdata$interval), na.rm=TRUE, mean)
plot(interval, avgstepsbyinterval, type="l", xlab="interval", ylab="Average Steps by Interval")
3.2: Interval has maximal avg of numbers of steps
newdata<-data.frame(interval, avgstepsbyinterval)
colnames(newdata)<-c("interval", "avgsteps")
newdata<-transform(newdata, interval=factor(interval))
intervalwithmaxvagsteps<-subset(newdata, avgsteps==max(avgstepsbyinterval))
intervalwithmaxvagsteps## interval avgsteps
## 835 835 206.1698step4: Imputing missing values
4.1: Calculate and report the total number of missing values in the dataset
#Total number of missing values
good<-complete.cases(repdata)
sum(!good)## [1] 2304#General summary of missing values
table(!good)##
## FALSE TRUE
## 15264 2304#Summary for each column with missing values
colSums(is.na(repdata))## steps date interval
## 2304 0 04.2: Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.
#A function to replace NA value with the mean for that interval
newrepdata<-data.frame()
for (i in interval){
dat<-subset(repdata, repdata$interval==i)
substeps <-subset(repdata$steps,repdata$interval==i)
dat[is.na(dat)] <- mean(substeps, na.rm=TRUE)
newrepdata<-rbind(newrepdata, dat)
}4.3: Create a new dataset that is equal to the original dataset but with the missing data filled in.
## rearrange the data according to date and interval
# Load "dplyr" package
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionnewrepdata<-arrange(newrepdata, date, interval)
## Compare the new and old datasets
summary(newrepdata)## steps date interval
## Min. : 0.00 2012-10-01: 288 Min. : 0.0
## 1st Qu.: 0.00 2012-10-02: 288 1st Qu.: 588.8
## Median : 0.00 2012-10-03: 288 Median :1177.5
## Mean : 37.38 2012-10-04: 288 Mean :1177.5
## 3rd Qu.: 27.00 2012-10-05: 288 3rd Qu.:1766.2
## Max. :806.00 2012-10-06: 288 Max. :2355.0
## (Other) :15840summary(repdata)## steps date interval
## Min. : 0.00 2012-10-01: 288 Min. : 0.0
## 1st Qu.: 0.00 2012-10-02: 288 1st Qu.: 588.8
## Median : 0.00 2012-10-03: 288 Median :1177.5
## Mean : 37.38 2012-10-04: 288 Mean :1177.5
## 3rd Qu.: 12.00 2012-10-05: 288 3rd Qu.:1766.2
## Max. :806.00 2012-10-06: 288 Max. :2355.0
## NA's :2304 (Other) :158404.4: Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?
## Make a histogram of the total number of steps taken each day
newtotalStepPerDay<-tapply(newrepdata$steps, newrepdata$date, sum)
hist(newtotalStepPerDay, col="blue", xlab= "Total Steps Per Day", main="Summary of Total Steps")
## Mean of the total number of steps taken per day
mean(newtotalStepPerDay, na.rm=TRUE)## [1] 10766.19## Median of the total number of steps taken per day
median(newtotalStepPerDay, na.rm=TRUE)## [1] 10766.19Step5: Comparison of activity patterns between weekdays and weekends
## Formate date
dt<-as.Date(as.vector(newrepdata$date), "%Y-%m-%d")
## Create a new factor variable in the dataset with two levels - "weekday" and "weekend"
wkd<-c('Saturday', 'Sunday')
wd<-factor((weekdays(dt) %in% wkd),
levels=c(FALSE, TRUE), labels= c("Weekday", "Weekend"))
## Add one variable into the dataset
newrepdata$weekday<-wd
## Remove the "date" variable
newrepdata<-newrepdata[,-2]
## Summarize data by "weekday" and "interval" variables
grp_cols<-names(newrepdata)[-1]
dots<-lapply(grp_cols, as.symbol)
sumdata<-newrepdata %>% group_by_(.dots=dots) %>% summarize(avgstep=mean(steps))
## transform "weekday" variable to factor
sumdata<-transform(sumdata, weekday=factor(weekday))
## check summarized data
head(sumdata)## interval weekday avgstep
## 1 0 Weekday 2.25115304
## 2 0 Weekend 0.21462264
## 3 5 Weekday 0.44528302
## 4 5 Weekend 0.04245283
## 5 10 Weekday 0.17316562
## 6 10 Weekend 0.01650943## Plot data with lattice package
library(lattice)
xyplot(avgstep ~ interval|weekday, data=sumdata, type = "l", layout=c(1, 2),
xlab="Interval", ylab="Average Steps by Interval", main = "Activity Patterns")